Data Science Skills and Possessing Proficiency in the Skills

In our ongoing study of data scientists, we ask data professionals to indicate their proficiency in 25 different data science skills. The 25 skills, listed in Figure 1, reflect the set of skills that are commonly associated with data scientists. These skills are, in fact, included in the studies listed above.

I used the "intermediate" proficiency level as the criterion for the data professional for possessing the skill. The proficiency level of "intermediate" indicates that a data professional is able to complete tasks as requested and can usually perform the skill without help from others.

I ranked the list of these 25 skills based on the percentage of data professionals who possessed each skill.

This list appears in Figure 2. The first 10 skills listed in the figure (from left to right) were the most common skill across all data professionals. The top 10 data science skills were:

S - Communication (87% possess skill)

T - Managing Structured data (75%)

M - Math (71%)

B - Project management (71%)

S - Data Mining and Viz Tools (71%)

S - Science/Scientific Method (65%)

S - Data Management (65%)

B - Product design and development (59%)

S - Statistics and statistical modeling (59%)

B - Business development (53%)

Many of the top data science skills fall into the areas of Statistics; all five statistics skills appear in the top 10 list, including Communication, Data Mining and Viz Tools, Science/Scientific Method and Statistics and statistical modeling. Additionally, three skills related to Business acumen appear in the top 10, including Project management and Product design and development. No Programming skills appear in the top 10 list.

Top 10 Data Science Skills Vary by Job Role

Next, I looked at the top data science skills by job role. This depiction also appears in Figure 2 (and Table 1 below in detail). For each of the job roles, I graphically indicated the frequency with which the data professionals in a particular role possessed the skills. As you can see in Figure 2, some top data science skills are common across all the different roles. These include Communication, Managing structured data, Math, Project management Data mining and viz tools, Data management and Product design and development. In addition to these similarities, however, there are considerable differences in top data science skills across the job roles. Let's take a look at each job role.

Top skills that are unique to Developers are related to skills in Technology and Programming. These top unique skills include Back-end programming, Systems Administration and Design and Database administration. While these data professionals possess these skills, fewer of them possess skills in other technology- and programming-heavy skills that are important in our Big Data world. For example, less than half possess skills in Cloud Management (42%), Big and Distributed Data (48%) and NLP and text mining (42%). These results are consistent with the data science study by RJ Metrics. I suspect that these percentages will grow as more graduates of data science programs come into the work world.

Creatives do not have top skills that are unique to only them. In fact, their list of top data science skills closely match those of Researchers. Of their 10 top list of data skills, they share eight of them.

Top data science skills for Researchers are primarily in the area of Statistics. Additionally, top data science skills that are unique to Researchers are highly quantitative in nature, including Machine Learning and Optimization.

Summary and Conclusions

Table 1. Top Data Science Skills by Job Role. Click image to enlarge.

The list of top data science skills depends on the type of data scientists you are considering. While some data science skills appear to be common among different types of data professionals (i.e., Communication, Managing structured data, Math, Project management Data mining and viz tools, Data management and Product design and development), other data science skills are unique to certain roles. Developers' top skills include Programming skills; Researchers' top skills include Math-heavy skills; Still Business Managers' top skills include business-related skills.

These results have implications for data professionals interested in the field of data science as well as the recruiters in pursuit of them and the organizations who hire them. Data professionals could use the results to understand they types of skills they need to possess for different types of jobs. If you have strong Statistics skills, you might look for jobs that have a strong research component. Know your skills and look for jobs where they can be highlighted.

Recruiters need to understand the different types of data science roles to better recruit data professionals that best match the role requirements for job openings. Avoid focusing on job titles of applicants; rather, identify their skills that match the requirement of job openings. Organizations can optimize their data science teams by ensuring they consist of different types of data scientists, each bringing specific talents to solve problems.

Data scientists possess many different skills. Some skills are very popular across different types of data scientists. But how important are those skills to project success? Are some skills more critical than other skills? I will be answering those questions next week.

[…] with which professionals possessed the skills. This method identified data science skills that are common across data scientists. The second way based on the correlation between the data scientists’ proficiency in the skill […]

[…] which professionals possessed the skills. This method identified data science skills that are common across data scientists. The second way based on the correlation between the data scientists' proficiency in the skill and […]

About me

I am Business Over Broadway (B.O.B.). I like to solve problems through the application of the scientific method. I use data and analytics to help make decisions that are based on fact, not hyperbole. My interests are at the intersection of customer experience, data science and machine learning. To learn more about me and what I do, click here.